The human brain is composed of nearly 100 billion neurons. Each neuron generates signals in the form of time-varying electrochemical potentials across their cellular membrane (action potentials). Due to its electrochemical nature, the timescale on which a single neural event takes place is orders of magnitude slower than electronic interactions implemented in a computer. Nonetheless, the human brain can leverage the interactions of many neurons to perform highly parallel computations. These complex signal cascades are responsible for all of the brain's information processing capabilities.
While neural circuits require the parallel action of many thousands, tens of thousands, hundreds of thousands, or millions of individual neurons, currently known methods for studying such circuits are typically only capable of measuring individual neural signals from a limited number of cells at a time. This limits understanding of how networks of many neurons give rise to the wide range of tasks performed by the brain. Thus, there is a need for systems and methods that are capable of detecting and processing neural events from a very large number of individual neurons. The results of the neural processing can provide a more in-depth understanding of how the brain functions. Such an understanding may allow treatment of disorders of the nervous system such as blindness, paralysis, and neurodegenerative diseases. Additionally, increasing the scale of interaction between man-made electronics and the human brain could lead to a new generation of high data rate brain-machine interfaces (BMIs) that can control complex prosthetics or mediate sensory input to the brain from devices such as digital cameras or microphones.
Processing information from a large number of individual neurons is a technological challenge due to the large amount of information generated. Continuous analog-to-digital conversion (ADC) of each neural action potential typically requires ADCs with high bit depths and sample rates. To process information from many thousands, tens of thousands, hundreds of thousands, or millions of neurons, bulky electronics and cables may be required, which may restrict body movement. In other instances, attempts to transmit fully sampled signals wirelessly from an implanted neural probe using current wireless technologies may cause undesirable heating of electronic circuits in the neural probe, and possibly result in damage to neural tissue.
Therefore, there is a need for systems and methods that are capable of detecting and processing neural events from a very large number of neurons, in a manner that minimizes the amount of information transmitted and heating emitted from an implanted neural probe while retaining enough information to generate useful data about the signals generated by many thousands, tens of thousands, hundreds of thousands, or millions of neurons.